Tutorial: using NEURON for neuromechanical simulations
The dynamical properties of the brain and the dynamics of the body strongly influence one another. Their interaction generates complex adaptive behavior. While a wide variety of simulation tools exist for neural dynamics or biomechanics separately, there are few options for integrated brain-body mod...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Computational Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2023.1143323/full |
| _version_ | 1797768593608278016 |
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| author | Chris Fietkiewicz Robert A. McDougal Robert A. McDougal Robert A. McDougal Robert A. McDougal David Corrales Marco Hillel J. Chiel Hillel J. Chiel Hillel J. Chiel Peter J. Thomas Peter J. Thomas Peter J. Thomas Peter J. Thomas Peter J. Thomas |
| author_facet | Chris Fietkiewicz Robert A. McDougal Robert A. McDougal Robert A. McDougal Robert A. McDougal David Corrales Marco Hillel J. Chiel Hillel J. Chiel Hillel J. Chiel Peter J. Thomas Peter J. Thomas Peter J. Thomas Peter J. Thomas Peter J. Thomas |
| author_sort | Chris Fietkiewicz |
| collection | DOAJ |
| description | The dynamical properties of the brain and the dynamics of the body strongly influence one another. Their interaction generates complex adaptive behavior. While a wide variety of simulation tools exist for neural dynamics or biomechanics separately, there are few options for integrated brain-body modeling. Here, we provide a tutorial to demonstrate how the widely-used NEURON simulation platform can support integrated neuromechanical modeling. As a first step toward incorporating biomechanics into a NEURON simulation, we provide a framework for integrating inputs from a “periphery” and outputs to that periphery. In other words, “body” dynamics are driven in part by “brain” variables, such as voltages or firing rates, and “brain” dynamics are influenced by “body” variables through sensory feedback. To couple the “brain” and “body” components, we use NEURON's pointer construct to share information between “brain” and “body” modules. This approach allows separate specification of brain and body dynamics and code reuse. Though simple in concept, the use of pointers can be challenging due to a complicated syntax and several different programming options. In this paper, we present five different computational models, with increasing levels of complexity, to demonstrate the concepts of code modularity using pointers and the integration of neural and biomechanical modeling within NEURON. The models include: (1) a neuromuscular model of calcium dynamics and muscle force, (2) a neuromechanical, closed-loop model of a half-center oscillator coupled to a rudimentary motor system, (3) a closed-loop model of neural control for respiration, (4) a pedagogical model of a non-smooth “brain/body” system, and (5) a closed-loop model of feeding behavior in the sea hare Aplysia californica that incorporates biologically-motivated non-smooth dynamics. This tutorial illustrates how NEURON can be integrated with a broad range of neuromechanical models.Code available athttps://github.com/fietkiewicz/PointerBuilder. |
| first_indexed | 2024-03-12T20:56:36Z |
| format | Article |
| id | doaj.art-f067fb0e7a3d4fbbb902358e4d267591 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-03-12T20:56:36Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-f067fb0e7a3d4fbbb902358e4d2675912023-07-31T16:10:36ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882023-07-011710.3389/fncom.2023.11433231143323Tutorial: using NEURON for neuromechanical simulationsChris Fietkiewicz0Robert A. McDougal1Robert A. McDougal2Robert A. McDougal3Robert A. McDougal4David Corrales Marco5Hillel J. Chiel6Hillel J. Chiel7Hillel J. Chiel8Peter J. Thomas9Peter J. Thomas10Peter J. Thomas11Peter J. Thomas12Peter J. Thomas13Department of Mathematics and Computer Science, Hobart and William Smith Colleges, Geneva, NY, United StatesDepartment of Biostatistics, Yale School of Public Health, New Haven, CT, United StatesWu Tsai Institute, Yale University, New Haven, CT, United StatesProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United StatesSection for Biomedical Informatics, Yale School of Medicine, New Haven, CT, United StatesDepartment of Mathematics and Computer Science, Hobart and William Smith Colleges, Geneva, NY, United StatesDepartment of Biology, Case Western Reserve University, Cleveland, OH, United StatesDepartment of Neurosciences, Case Western Reserve University, Cleveland, OH, United StatesDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United StatesDepartment of Biology, Case Western Reserve University, Cleveland, OH, United StatesDepartment of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, OH, United States0Department of Cognitive Science, Case Western Reserve University, Cleveland, OH, United States1Department of Electrical, Control, and Systems Engineering, Case Western Reserve University, Cleveland, OH, United States2Department of Data and Computer Science, Case Western Reserve University, Cleveland, OH, United StatesThe dynamical properties of the brain and the dynamics of the body strongly influence one another. Their interaction generates complex adaptive behavior. While a wide variety of simulation tools exist for neural dynamics or biomechanics separately, there are few options for integrated brain-body modeling. Here, we provide a tutorial to demonstrate how the widely-used NEURON simulation platform can support integrated neuromechanical modeling. As a first step toward incorporating biomechanics into a NEURON simulation, we provide a framework for integrating inputs from a “periphery” and outputs to that periphery. In other words, “body” dynamics are driven in part by “brain” variables, such as voltages or firing rates, and “brain” dynamics are influenced by “body” variables through sensory feedback. To couple the “brain” and “body” components, we use NEURON's pointer construct to share information between “brain” and “body” modules. This approach allows separate specification of brain and body dynamics and code reuse. Though simple in concept, the use of pointers can be challenging due to a complicated syntax and several different programming options. In this paper, we present five different computational models, with increasing levels of complexity, to demonstrate the concepts of code modularity using pointers and the integration of neural and biomechanical modeling within NEURON. The models include: (1) a neuromuscular model of calcium dynamics and muscle force, (2) a neuromechanical, closed-loop model of a half-center oscillator coupled to a rudimentary motor system, (3) a closed-loop model of neural control for respiration, (4) a pedagogical model of a non-smooth “brain/body” system, and (5) a closed-loop model of feeding behavior in the sea hare Aplysia californica that incorporates biologically-motivated non-smooth dynamics. This tutorial illustrates how NEURON can be integrated with a broad range of neuromechanical models.Code available athttps://github.com/fietkiewicz/PointerBuilder.https://www.frontiersin.org/articles/10.3389/fncom.2023.1143323/fullbrainbodymotor controlneural networkclosed-loopbiomechanics |
| spellingShingle | Chris Fietkiewicz Robert A. McDougal Robert A. McDougal Robert A. McDougal Robert A. McDougal David Corrales Marco Hillel J. Chiel Hillel J. Chiel Hillel J. Chiel Peter J. Thomas Peter J. Thomas Peter J. Thomas Peter J. Thomas Peter J. Thomas Tutorial: using NEURON for neuromechanical simulations Frontiers in Computational Neuroscience brain body motor control neural network closed-loop biomechanics |
| title | Tutorial: using NEURON for neuromechanical simulations |
| title_full | Tutorial: using NEURON for neuromechanical simulations |
| title_fullStr | Tutorial: using NEURON for neuromechanical simulations |
| title_full_unstemmed | Tutorial: using NEURON for neuromechanical simulations |
| title_short | Tutorial: using NEURON for neuromechanical simulations |
| title_sort | tutorial using neuron for neuromechanical simulations |
| topic | brain body motor control neural network closed-loop biomechanics |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2023.1143323/full |
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